To address this problem, we hypothesize that the optimal size of the neighborhood can be different in the training and inference stages. This is because training, by nature, tends to benefit from more extensive data, whereas relevance may play a more critical role in inference. In accordance with the conventional nearest-neighbor approaches [1]}, [2]}, [3]}, we utilize more neighbors for training and focus on a smaller neighborhood with strongly connected target users for inference. This strategy can be regarded as data augmentation to improve the training of local models, representing small and coherent sub-communities. However, unlike existing data augmentation that synthetically generates virtual users, we choose real users from the neighborhood in the entire matrix.
